Momentum extrapolation prediction-based asynchronous distributed optimization for power systems

摘要

Iterative distributed optimization algorithms usually need synchronization of subproblems at each iteration. An iteration index is defined, and subproblems are solved once at each iteration. This degrades distributed optimization scalability and computational resource under-utilization, particularly if subproblems are heterogeneous. To address these limitations for solving optimal power flow, this paper proposes a prediction-correction-based asynchronous alternating direction method of multipliers (A-ADMM). At the end of each iteration, an OPF subproblem no longer needs to wait for its neighbors' most updated shared variable information. A momentum-based extrapolation method is developed to predict shared variable values. A correction step is designed using momentum to prevent predicted values from becoming far from the possible solution and avoid divergence. These predictions are integrated into distributed optimization to allow subproblems to be solved continuously with no need for synchronization at each iteration. The proposed A-ADMM reduces the unproductive time and computational resource under-utilization if subproblems are computationally heterogeneous. A-ADMM potentially enhances the solution speed even if subproblems are homogeneous as every iteration k is carried out using a good forecast of shared variable values in iteration k + 1. Numerical results show the promising performance of the proposed algorithm.